FIG. 1 shows a section of a typical ECG waveform containing two normal beats and one abnormal beat. Methods and apparatus are known for the quantification of QRS morphology in ECG signals using a small set of numbers or features to aid in detecting, for example, premature ventricular contractions (PVCs) and in quantifying cardiac arrhythmias. In general, morphology information is used to group heart beats into similar classes, so that information from morphologically similar beats can be used to assist in judging whether a given beat is normal or abnormal, e.g., a PVC.
In a conventional QRS feature extraction system, the first step is QRS detection. Once a putative QRS is detected, a segment of the ECG data is pulled out in a window surrounding the time of detection. The ECG data in the window, e.g., a QRS pattern vector, is then transformed into features which describe QRS morphology, e.g., the QRS feature vector.
A number of techniques for translating the QRS pattern vector into a small number of features have been proposed and implemented. U.S. Pat. No. 4,336,810, for example, discloses a method and apparatus for arrhythmia analysis of ECG recordings wherein a magnetically recorded ECG tape is scanned for QRS complexes. The scanned ECG signal is converted into a digital representation and then compared to previously known QRS patterns which are grouped together as templates. If no match is found for the current QRS complex, a new template is created and the next QRS complex is scanned and classified. To classify each QRS complex, characteristics of a QRS complex's shape are used, which include the width of the complex and the area above and below the baseline of the QRS complex. Once each QRS complex is classified using the templates, the complexes are labeled as normal, supraventricular ectopic, ventricular ectopic or unknown. U.S. Pat. No. 4,583,553 discloses an ambulatory ECG analyzer and recorder wherein a patient's ECG signal is digitized and filtered by hardware circuitry and then used as an input for algorithm, which detects a QRS complex, classifies it, groups it into a collection of like complexes and generates a report based on its findings. An algorithm, which is designed to work in real time, detects up to 43 events, but prioritizes the events based on a particular patient. The algorithm uses a two channel ECG to minimize classification errors.
U.S. Pat. No. 4,742,458 discloses a method and apparatus for performing pattern recognition analysis wherein an ECG signal is filtered to allow hardware circuitry to analyze a QRS complex within an ECG signal. Two features of the QRS complex, which include slope transitions and intervals between slope transitions, are used to create a signature for the ECG signal. Once a QRS complex is captured and classified, it is grouped into a classification of like complexes or, if no match is found, a new classification is created.
U.S. Pat. No. 4,589,420 discloses a method and apparatus for ECG rhythm analysis using an algorithm which detects a QRS complex within an ECG signal, classifies it and uses it to determine characteristics about a patient's condition. A number of different characteristics of a QRS complex are extracted from the ECG signal, including QRS complex width, R-R interval and instantaneous and averaged heart rates. A determination and classification of the complex is made based on the amount of noise present within the complex. Beats are classified using a two step process wherein each new QRS complex is tested against previous complexes and then by a finite state machine process based on the results of the previous complex matching step.